Meta-Kernel Convolution.
As argued by Fan et al.  [1]}, using 2D convolution on range images cannot fully exploit the 3D geometric information due to the dimensionality reduction of the spherical projection.
Exploiting meta-kernel convolution, we can take advantage of the 3D geometric information by using the relative Cartesian coordinates of the \(3\!\times \!3\)  neighbors of the center, as shown in Fig. REF .
A shared MLP takes these relative coordinates as input to generate nine weight vectors \(w_j\) , and does element-wise product on the corresponding nine feature vectors \(f_j\) .
Finally, by passing a concatenation of the nine neighbors output \(g_j\)  to a \(1\!\times \!1\)  convolution, we aggregate the information from different channels and different sampling locations to update the center feature vector.
